Instructions to use SEBIS/code_trans_t5_base_program_synthese_multitask_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_base_program_synthese_multitask_finetune with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="SEBIS/code_trans_t5_base_program_synthese_multitask_finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_multitask_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_multitask_finetune") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2a000f108f619013af40c1db1e88168b0a25350f76d9bb7ecee70bb2d7a9742
- Size of remote file:
- 892 MB
- SHA256:
- 02883ded21e09b33de8a89d66ae4befedd1c529b25309bafef8578610fe3a84f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.